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Infrared Ship Video Target Tracking Based on Cross-Connection and Spatial Transformer Network

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

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Abstract

In this paper, to improve the efficiency of infrared (IR) ship target tracking, an efficient SiamRPN++ method based on AlexNet with cross connection and spatial transformer network is proposed. The cross-connection method integrates the features of shallow layers and the deep layers to increase the spatial information of the output features. To reduce the influence of target rotation and scaling on tracking accuracy, we introduced the spatial transformer network to explicitly learn rotation invariance, which can supplement the implicit rotation invariance learned by convolutional neural network. Moreover, in order to train and evaluate the model more appropriately and to deal with the problem of the lacking IR ship video target tracking data set, we constructed an IR ship video tracking data set including 6725 frames of images. The experimental results show that the proposed method can effectively improve the speed to 63.9 FPS, which is 9.7 times faster than the SIAMRPN++ method under the condition of ensuring the accuracy and the average intersection of union (mIoU).

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Funding

This work is supported by the National Natural Science Foundation of China (61806013, 61906005), General project of Science and Technology Plan of Beijing Municipal Education Commission (KM202110005028), International Research Cooperation Seed Fund of Beijing University of Technology (2021A01).

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Authors and Affiliations

  1. Faculty of Information Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Zhaoying Liu, Junran He, Ting Zhang & Ran Tang

  2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China

    Yujian Li

  3. School of Engineering, Edith Cowan University, Perth, WA, 6027, Australia

    Muhammad Waqas

Authors
  1. Zhaoying Liu
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  2. Junran He
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  3. Ting Zhang
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  4. Ran Tang
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  5. Yujian Li
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  6. Muhammad Waqas
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Corresponding author

Correspondence to Ting Zhang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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We declare that we have no actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations that can inappropriately influence our work.

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Liu, Z., He, J., Zhang, T., Tang, R., Li, Y., Waqas, M. (2022). Infrared Ship Video Target Tracking Based on Cross-Connection and Spatial Transformer Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-06788-4

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